tensorflow-image-classifier | Easily train an image classifier | Machine Learning library

by damianmoore Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tensorflow-image-classifier Summary

tensorflow-image-classifier is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow-image-classifier has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However tensorflow-image-classifier build file is not available. You can download it from GitHub.

Easily train an image classifier and then use it to label/tag other images

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tensorflow-image-classifier has a low active ecosystem.

It has 24 star(s) with 5 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

tensorflow-image-classifier has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-image-classifier is current.

Quality

tensorflow-image-classifier has 0 bugs and 0 code smells.

Security

tensorflow-image-classifier has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-image-classifier code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow-image-classifier is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

tensorflow-image-classifier releases are not available. You will need to build from source code and install.

tensorflow-image-classifier has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

tensorflow-image-classifier saves you 260 person hours of effort in developing the same functionality from scratch.

It has 630 lines of code, 21 functions and 2 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed tensorflow-image-classifier and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow-image-classifier implemented functionality, and help decide if they suit your requirements.

Retrieve bottleneck bottlenecks
Ensure directory exists
Get or create a bottleneck
Returns the full path to the image
Run bottleneck on an image
Returns the path to the bottleneck
Create bottleneck file
Adds the final training
Add summaries for a variable
Download and extract a tarball
Creates the bottleneck graph
Adds evaluation step
Adds input distortions
Cache bottleneck bottlenecks
Returns a list of bottleneck_truths
Creates a list of image lists
Determines if the image should distort

Get all kandi verified functions for this library.

tensorflow-image-classifier Key Features

No Key Features are available at this moment for tensorflow-image-classifier.

tensorflow-image-classifier Examples and Code Snippets

No Code Snippets are available at this moment for tensorflow-image-classifier.

Community Discussions

Trending Discussions on tensorflow-image-classifier

How to feed an image ROI into session.run() of Tensorflow?

QUESTION

How to feed an image ROI into session.run() of Tensorflow?

Asked 2019-Mar-12 at 12:19

I am trying to feed my image roi into the Tensorflow classifier I took from here. The idea is to first run a simple filter, get rectangle candidates, and then check (using the network) whether each rectangle(roi) is actually what I am looking for.

...

ANSWER

Answered 2019-Mar-08 at 15:12

feed_dict expect a dictionary with tensors as keys, to populate the placeholders with the specified valued. It's not in your code snippet how does the screw_id is initiated, but I bet it's not a tensor of any kind, hence, your error.

Source https://stackoverflow.com/questions/55065471

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tensorflow-image-classifier

The best way to get started is to try it out with an example dataset. Here we download a labelled dataset of flowers, move the photos into position, train the model against the dataset then use it to classify an image.
You need a directory that will hold the tensorflow data, your images and the output graphs - this is usually called tf_files. Make a classifier directory in it which is the name of the classifier you are building. Inside that should be a directory data where you should put directories for each of the classification labels and then images within those.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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